Strategic advice & alerts to leverage data analytics & new technologies
Leverage data and the technologies that generate it, from IoT to AI/machine learning, wearables, blockchain, and more, to improve decision-making, enrich collaboration and enable new services.
We have been confronted for nearly two decades with a dual data storage and processing landscape, supported by two very distinct scenes of tool vendors and products. Nowadays, we see a complete convergence of the operational and tactical/strategic data needs of the corresponding data integration tooling. This evolution poses interesting challenges to the landscape of data storage and data integration solutions.
In this Executive Update, we recommend some Agile techniques and best practices that retain the benefits of a central EDW while reducing the expense and lack of responsiveness to business needs and change. This data-focused approach offers a way to define user stories in a complex EDW architecture, addressing both application and information value to users and delivering a data warehouse that provides high-quality information and is resilient to change.
In this Executive Update, we devise a conceptual model and practical design guidelines for the holistic management of all resources (e.g., servers, networks, storage, cooling systems) to improve energy efficiency and to reduce the carbon footprints in cloud data centers (CDCs). Furthermore, we discuss the intertwined relationship between energy and reliability for sustainable cloud computing, where we highlight the associated issues. Finally, we propose a set of future areas to investigate in the field and propose further practical developments.
In an ongoing Cutter Consortium survey covering the adoption and application of AI technology, we asked organizations about their plans for using AI-as-a-Service platforms and services.
In this Update, I delve deeper into the importance of the level at which analytics are performed — in particular, the need to pay attention to two keywords: granularity and context. In the absence of awareness of granularity and context, analytics may not provide the necessary value to the business and, as a result, increase business risks.
Building AI systems is a huge undertaking. Therefore, most companies should focus on helping employees adjust to the new world of AI, curating the right data and leaving the mechanics of building AI systems to vendors.
This Executive Update discusses the thinking behind employing artificial intelligence (AI) in an organization through “augmentation.” It presents a case study on how a superregional bank implemented a cognitive contact center by using an AI framework called HALO — Human Augmented Learning Organization — and showcases the meaning of “AI as a practice” within an organization.
Whether AI eventually lives up to all the hype obviously remains to be seen; however, I expect that we are going to witness some innovative and disrupting applications in the not-too-distant future.